AI in Software Testing – It’s undeniable that AI has become an integral part of our daily lives. From virtual assistants like Siri and Alexa to self-driving cars and smart homes, AI is revolutionizing the way we live, work, and interact with technology. Companies like Amazon, eBay use AI and machine learning algorithms to analyze customer behavior and provide personalized shopping recommendations, improving user experience and increasing sales.
What’s the impact of AI in software testing?
Artificial intelligence (AI) is rapidly changing the way software is developed and tested. AI-powered tools can automate many of the tasks that are currently done by humans, freeing up testers to focus on more strategic and creative work. AI can also help to improve the quality and reliability of software by identifying and fixing bugs more quickly and efficiently.
In this article, we will look into the AI landscape, and how organizations make the most of AI in software testing. We will also discuss some of the challenges that need to be addressed in order to make AI-powered testing a reality and at last some handy tools to help you get started with AI in testing right away.
Role of AI in testing:
AI in software test automation is primarily focused on making the SDLC easier. Through the application of reasoning, problem-solving, and in some cases, machine learning, AI can help automate tasks in development and testing, thereby reducing the direct involvement of developers or testers in mundane tasks.
The use of AI in Software Testing is still evolving. However, in terms of software automated testing, it is not as extensively utilized as it is in other sophisticated fields such as self-driving systems, voice-assisted control, and healthcare.
For instance, most test automation tools run tests and deliver results, but they don’t know which tests to run. They either run all of them or a predetermined set. However, AI-powered software can make informed decisions about which tests to run by considering a variety of factors. This decision-making based on changing data is an example of applying AI in software testing. Now, AI can also be used to make decisions regarding software testing strategies, including prioritizing tests and proposing new test cases.
Real Examples of ML & AI in Software Testing
Although AI Testing adaptation is at mass, every organization trying to blend in the mix to ease testing labor and reduce testing time. Here are some real-life examples of how big names are revolutionizing their way of testing with AI.
Facebook uses Sapienz for automated software testing at scale
Facebook uses an AI-powered system called Sapienz, which automates software testing for its apps. It leverages a technique known as search-based software testing to navigate through the app like a user, finding crashes or errors. This reduces the workload of human testers and increases the efficiency of Facebook’s app deployment.
Automated Canary Analysis at Netflix
Netflix uses automated canary analysis, an ML-driven method to detect and respond to any changes that might degrade the user experience. It helps the company identify if a newly deployed code will cause any problems before it is rolled out to the entire user base.
Google’s DeepMind for Game Testing
Google’s DeepMind has been used to test video games. Its advanced AI algorithms have been successful in identifying potential bugs and issues in complex video games like Go, StarCraft II, and Atari games. With their open test environments readily available for researchers to use, it is evident that AI has the potential to revolutionize the gaming industry, allowing for more efficient and accurate bug identification and resolution.
TestGrid Uses AI to deliver effortless Codeless Testing
TestGrid uses AI/ML to unleash the fastest scriptless testing experience. Users can focus on building logical workflows and leave the test creation to TestGrid. TestGrid can interpret a simple test scenario explained in English and generate an automated script respectively which empowers all stakeholders to test their native or web app effortlessly.
It also has an auto-heal feature that repairs any broken test by identifying the failure of the test build with the help of AI and fixing it on its own, saving you hours of manual bandwidth which would have been utilized in debugging and fixing the failure otherwise.
Input Story –
- The Facebook website is launched
- Check email is visible
- Enter firstname.lastname@example.org in the email input field.
- Enter test@1234 in the password input field.
- Click login button
- Check the password you entered incorrect messages
Output Testcase –
Benefits of Leveraging AI in Testing
Reducing Time and Effort
The AI- driven tests can quickly go through log files, scan code in a matter of seconds, and find faults far more quickly than manual testers. It can identify bugs more quickly and easily than traditional manual testing methods. These AI-driven tests are especially valuable for testing large and complex software systems that would be challenging for manual testing.
Automate Script Updates for UI Changes
Businesses frequently make changes to the user interface (UI) of their apps.Even minor UI changes can cause test scripts to fail. AI and machine learning algorithms can be trained to detect UI changes.These algorithms focus on identifying alterations in UI elements like buttons, forms, menus, and other visual components. The test scripts can be automatically updated by these algorithms to reflect the changes. This can save time and effort for testers.
AI Testing helps software systems to be tested thoroughly, including all the possible use cases. It ensures that all the software bugs have been detected, which ultimately results in a more robust and reliable software program.
AI-driven tests enable continuous testing, and thus products are released faster, ensuring businesses go to market early.
What are the key AI-driven testing approaches?
These are some AI-driven testing approaches that are transforming the way we ensure the quality and reliability of software applications.
1. Intelligent Test Case Generation: AI testing tools can automatically create test cases based on software’s specifications and the most frequently used scenarios on your website. This approach can boost testing coverage and ensure that all critical scenarios are tested.
TestGrid’s AI algorithms generate test cases rapidly, saving up to 80% of the time typically spent on manual test case creation. Book a demo
2. Predictive Analytics: AI can be used to predict potential areas of failure in the software by analyzing data from previous test runs and software usage. This approach allows testers to focus their efforts on the areas that are most likely to have issues, improving the efficiency and effectiveness of testing.
3. Visual testing: Visual Testing uses AI to identify the visual element defects in software applications. AI can be used to make sure that visual elements such as images, colors, and fonts are displayed accurately and consistently across various devices, operating systems, and browsers.
4. Defect Analysis and Prediction: AI can be used to analyze historical data to identify the different patterns and predict where defects can occur in future releases.
5. Self-Healing Test Automation: This type of automation can automatically correct errors in test scripts when the UI of the software application changes with the help of AI-powered algorithms. This can be accomplished by identifying the objects and properties that have changed and then upgrading the test scripts to reference the new objects and attributes.
AI Tools You Need to Be Aware of
TestGrid is an AI in Software Testing Tools platform that offers a variety of features to help you test your apps on real devices, including automated testing, performance testing, API testing, and security testing. Testgrid provides on-demand access to hundreds of real Android and iOS devices, giving you the flexibility to test your app on a wide range of devices.
Here are some of the benefits of using TestGrid for mobile app testing:
- TestGrid AI in Software Testing will generate test cases for you, all you need to provide is your test scenario in plain English.
- Access to a wide range of real Android and iOS devices.
- Test your app’s performance on devices with different battery life, network strengths, responsiveness, and swipe gestures.
- Create test cases in a codeless manner.
- Support for programming languages like PHP, Ruby, Python. Java, C# and . NET
An AI-powered chatbot that uses natural language processing (NLP) technology to understand and respond to human input in a conversational manner. It can be used to create chatbots, virtual assistants, and other applications that require human-like conversation capabilities.
Use cases in software testing
- Script Generation: It can be used to generate or help write test scripts. Given the scenario or user story, it can create a draft of the script that testers can then refine and add to the test suite.
- Test Case Descriptions: It can help in generating understandable and detailed test case descriptions. This can assist in bridging the gap between testers and stakeholders who are non-technical.
- Come Completion: Developers can benefit from its code completion capability to generate concise and efficient code snippets, reducing the overall size of the codebase.
- Error Explanation: When tests fail, the error logs can be hard to understand. It can help to interpret these logs and translate them into a more understandable format.
- Documentation: Documentation is a critical aspect of testing. ChatGPT can help to automate the process of keeping this documentation up-to-date, by translating changes in test scripts into changes in the documentation.
GitHub Copilot is an AI assistant that uses the OpenAI Codex to suggest code and entire functions in real-time. It is trained in all languages that appear in public repositories, so it can suggest code in a wide variety of languages and frameworks.
Use cases in software testing
- Help developers write more efficient and bug-free code.
- Automate repetitive tasks, such as code formatting and unit testing.
- Generate documentation and comments for code.
TensorFlow is a popular open-source machine learning framework developed by Google in 2015. It is designed to simplify the process of creating and training machine learning models. TensorFlow is one of the most commonly used machine learning frameworks, and it is widely used in industry and academic research.
Use cases in software testing
- Builds models that can predict defects and detect anomalies, allowing for proactive identification and resolution of issues.
- Classifies issues based on patterns in historical data, enabling more accurate and efficient issue tracking and resolution.
In conclusion, AI is fast becoming an essential part of software testing. It offers numerous benefits over traditional manual testing methods, including improved accuracy, increased efficiency, and enhanced test coverage. As we move towards a future driven by technologies like IoT, robotics, and quantum computing, the role of AI in software testing is set to become even more significant. It’s time to embrace AI in software testing and work smarter, not harder, to ensure the quality of our software applications.
TestGrid’s AI-powered testing platform can help you:
- Automate your tests so you can save time and resources.
- Automatically generate test cases based on your website or application’s structure, functions, and user interaction patterns.
- Test on real devices to ensure your software works as expected.
- Get AI-based insights into your test data so you can identify and fix bugs faster.
Sign up for free today and see for yourself how TestGrid can help you take your software testing to the next level.
1Q. How do I get started with implementing AI in my software testing process?
Ans: Start by assessing your current testing process, identifying areas where AI in Software Testing can add value, and developing a roadmap for implementation. It’s essential to upskill your team, invest in appropriate infrastructure, and choose the right AI-powered tools for your needs.
2Q. Can AI-powered tools handle unexpected issues during testing?
Ans: Yes, AI-powered tools can adapt to unexpected issues during testing by analyzing patterns and adjusting test cases accordingly.
3Q. What is AI testing?
Ans: AI testing refers to the use of artificial intelligence to automate and enhance software testing processes. It involves using AI in Software Testing algorithms and tools to perform tasks such as test case generation, test execution, and result analysis, which can improve the efficiency, accuracy, and coverage of testing.
4Q. How AI can support continuous testing?
Ans: AI in Software Testing can support continuous testing by automating repetitive tasks, improving the speed and accuracy of testing, and providing real-time feedback. AI-powered testing tools can execute test cases whenever changes are made to the software, ensuring that issues are detected and addressed as soon as possible.
CTO at TestGrid